{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "if(window['d3'] === undefined ||\n",
       "   window['Nyaplot'] === undefined){\n",
       "    var path = {\"d3\":\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\",\"downloadable\":\"http://cdn.rawgit.com/domitry/d3-downloadable/master/d3-downloadable\"};\n",
       "\n",
       "\n",
       "\n",
       "    var shim = {\"d3\":{\"exports\":\"d3\"},\"downloadable\":{\"exports\":\"downloadable\"}};\n",
       "\n",
       "    require.config({paths: path, shim:shim});\n",
       "\n",
       "\n",
       "require(['d3'], function(d3){window['d3']=d3;console.log('finished loading d3');require(['downloadable'], function(downloadable){window['downloadable']=downloadable;console.log('finished loading downloadable');\n",
       "\n",
       "\tvar script = d3.select(\"head\")\n",
       "\t    .append(\"script\")\n",
       "\t    .attr(\"src\", \"http://cdn.rawgit.com/domitry/Nyaplotjs/master/release/nyaplot.js\")\n",
       "\t    .attr(\"async\", true);\n",
       "\n",
       "\tscript[0][0].onload = script[0][0].onreadystatechange = function(){\n",
       "\n",
       "\n",
       "\t    var event = document.createEvent(\"HTMLEvents\");\n",
       "\t    event.initEvent(\"load_nyaplot\",false,false);\n",
       "\t    window.dispatchEvent(event);\n",
       "\t    console.log('Finished loading Nyaplotjs');\n",
       "\n",
       "\t};\n",
       "\n",
       "\n",
       "});});\n",
       "}\n"
      ],
      "text/plain": [
       "\"if(window['d3'] === undefined ||\\n   window['Nyaplot'] === undefined){\\n    var path = {\\\"d3\\\":\\\"https://cdnjs.cloudflare.com/ajax/libs/d3/3.5.5/d3.min\\\",\\\"downloadable\\\":\\\"http://cdn.rawgit.com/domitry/d3-downloadable/master/d3-downloadable\\\"};\\n\\n\\n\\n    var shim = {\\\"d3\\\":{\\\"exports\\\":\\\"d3\\\"},\\\"downloadable\\\":{\\\"exports\\\":\\\"downloadable\\\"}};\\n\\n    require.config({paths: path, shim:shim});\\n\\n\\nrequire(['d3'], function(d3){window['d3']=d3;console.log('finished loading d3');require(['downloadable'], function(downloadable){window['downloadable']=downloadable;console.log('finished loading downloadable');\\n\\n\\tvar script = d3.select(\\\"head\\\")\\n\\t    .append(\\\"script\\\")\\n\\t    .attr(\\\"src\\\", \\\"http://cdn.rawgit.com/domitry/Nyaplotjs/master/release/nyaplot.js\\\")\\n\\t    .attr(\\\"async\\\", true);\\n\\n\\tscript[0][0].onload = script[0][0].onreadystatechange = function(){\\n\\n\\n\\t    var event = document.createEvent(\\\"HTMLEvents\\\");\\n\\t    event.initEvent(\\\"load_nyaplot\\\",false,false);\\n\\t    window.dispatchEvent(event);\\n\\t    console.log('Finished loading Nyaplotjs');\\n\\n\\t};\\n\\n\\n});});\\n}\\n\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "#<File:/dev/null>"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "require 'ruby_brain'\n",
    "require 'nyaplot'\n",
    "org_stderr = $stderr\n",
    "$stderr = File.open(File::NULL, \"w\")\n",
    "org_stdout = $stdout\n",
    "$stdout = File.open(File::NULL, \"w\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this example, we tray to make a network which imitate a wave form.\n",
    "\n",
    "Currently RubyBrain has shigmoid as the activation function.\n",
    "Output layer also can has only sigmoid. \n",
    "In the result, the network can only treat 0~1 outpus.\n",
    "\n",
    "So, we prepare a function which produces values whithin 0~1 for this example."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Define `X` is  range $0 .. (2*\\pi)$ with step $0.1$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1, 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2, 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3, 0.31, 0.32, 0.33, 0.34, 0.35000000000000003, 0.36, 0.37, 0.38, 0.39, 0.4, 0.41000000000000003, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47000000000000003, 0.48, 0.49, 0.5, 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.5700000000000001, 0.58, 0.59, 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.6900000000000001, 0.7000000000000001, 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.8200000000000001, 0.8300000000000001, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.9400000000000001, 0.9500000000000001, 0.96, 0.97, 0.98, 0.99, 1.0]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = (0..1).step(0.01).to_a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set\n",
    "\n",
    "$(0.75 * sin(x*2*\\pi) + 0.15 * cos(5*\\pi – 0.023) + 1) / 2$\n",
    "\n",
    "to `Y_IDEAL`\n",
    "\n",
    "and use `Y_IDEAL + (random noise)` for `Y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.40552972386823233, 0.4774905499906632, 0.46219024504515543, 0.49408772854706584, 0.5511014641648844, 0.5830607265235062, 0.6290909985528079, 0.7188366408926009, 0.7464502651167544, 0.7570660940515662, 0.8487802767323701, 0.8048638528562673, 0.8781999845775225, 0.8350774438331358, 0.8231075782014501, 0.8205059962114565, 0.7620703948059987, 0.7592930665178563, 0.7564538805783714, 0.7718808660785458, 0.7939093724782238, 0.7241579038424741, 0.7556405318869907, 0.783810615332835, 0.8146499203453131, 0.9079803254799771, 0.8609454155246148, 0.963374223298478, 0.953686722421743, 0.9423887091039158, 0.9586439428093037, 0.9007886717482598, 0.8955021053214614, 0.9019821644047896, 0.8086460470276943, 0.8185435115568951, 0.7927944294792918, 0.7413519001413804, 0.6947676161553855, 0.6612751665879212, 0.6504900333096678, 0.5568179391111076, 0.6171380642298777, 0.5603384907700707, 0.5760947029063451, 0.5844655117589066, 0.6711066378344058, 0.5841094333299761, 0.5794228773540913, 0.5832987506533194, 0.5501273323962572, 0.6012162864215608, 0.5261450973336963, 0.4587469523684175, 0.4553581842601778, 0.37634597984408674, 0.31052441243765166, 0.2690342620110815, 0.2536487272472072, 0.24769930022299474, 0.17983166212931287, 0.12805859422706325, 0.11537112023884277, 0.21125790572283304, 0.16186838744714038, 0.16132579127049282, 0.2444294667072354, 0.19538817632744343, 0.24132425605822755, 0.22265883054824356, 0.2636831443649561, 0.26362289739592015, 0.2222927083908854, 0.17811329085234903, 0.12732948899046037, 0.16354600102188116, 0.13097407719532192, 0.06720693291100473, 0.05812709756878874, 0.01542796780553005, 0.07579910093602107, 0.07597297165054914, 0.057527311423150096, 0.08270797348892969, 0.10655437450419285, 0.23844271322818672, 0.2668974483956788, 0.2526080180629122, 0.36733263194408383, 0.3199785178151612, 0.3481489425022125, 0.4350143793173079, 0.37639065262225435, 0.3603462039844907, 0.34573400064588994, 0.4343252944140983, 0.3526295334202674, 0.3255975274840675, 0.3246599727949182, 0.3813989077499905, 0.36205386878698165]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_IDEAL = X.map {|x| (0.75 * Math.sin(x*2*Math::PI) - 0.2 * Math.cos(5*x*2*Math::PI - 0.023) + 1) / 2} \n",
    "Y = [Y_IDEAL, Array.new(X.size) {rand(-0.05..0.05)}].transpose.map {|e| e.inject(:+)}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Following graph shows `X`, `Y_IDEAL` and `Y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id='vis-0177102c-97a0-48fb-a810-5797bccb3233'></div>\n",
       "<script>\n",
       "(function(){\n",
       "    var render = function(){\n",
       "        var model = 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       "        var id_name = '#vis-0177102c-97a0-48fb-a810-5797bccb3233';\n",
       "        Nyaplot.core.parse(model, id_name);\n",
       "\n",
       "        require(['downloadable'], function(downloadable){\n",
       "          var svg = d3.select(id_name).select(\"svg\");\n",
       "\t  if(!svg.empty())\n",
       "\t    svg.call(downloadable().filename('fig'));\n",
       "\t});\n",
       "    };\n",
       "    if(window['Nyaplot']==undefined){\n",
       "        window.addEventListener('load_nyaplot', render, false);\n",
       "\treturn;\n",
       "    } else {\n",
       "       render();\n",
       "    }\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1 = Nyaplot::Plot.new\n",
    "y_ideal_line = plot1.add(:line, X, Y_IDEAL)\n",
    "y_ideal_line.title('Y_IDEAL')\n",
    "y_scatter = plot1.add(:scatter, X, Y)\n",
    "y_scatter.title('Y')\n",
    "y_scatter.color('rgb(43,140,190)')\n",
    "plot1.legend(true)\n",
    "plot1.show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use this `X` for input and `Y` for output for the training of the network.\n",
    "The structure of the network is `[1, 13, 6, 1]`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "#<File:best_weights_1470145958.yml (closed)>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_network = RubyBrain::Network.new([1, 13, 6, 1])\n",
    "a_network.init_network\n",
    "a_network.learning_rate = 0.5\n",
    "a_network.learn(X.map{|e| [e]}, Y.map{|e| [e]}, max_tra2ining_count=40000, tolerance=0.0004, monitoring_channels=[:best_params_training])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can review the trained network `a_network` with following code.\n",
    "`Y_PREDICATED` is the output corresponding to `X`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.3737866292325307, 0.4155508699326962, 0.4612227994051432, 0.510046222316126, 0.5607734887723744, 0.6116963934435019, 0.6608041726195469, 0.7060561912063408, 0.7456936081165836, 0.7784771369914162, 0.8037633916292516, 0.821410822089631, 0.831586492584581, 0.8345823617111626, 0.8307413200241917, 0.8205601443542602, 0.8049833967331003, 0.7858084350432145, 0.7659928349479052, 0.749589830496322, 0.7411587691845486, 0.7447336675059035, 0.7625310220377353, 0.7935930796595669, 0.8330121525085872, 0.873067538056558, 0.9065953289122177, 0.9300075375974213, 0.9432493802227251, 0.9475817667632656, 0.9436455216754961, 0.9308388250593038, 0.9080063638789239, 0.8751177181219204, 0.8346966936121065, 0.791409829361061, 0.7500728594939753, 0.7139692559515088, 0.6844888844303758, 0.6616316360791809, 0.6446489620789511, 0.6324921879080175, 0.6240459705698431, 0.6182210197904666, 0.6139740134240194, 0.6102956793165908, 0.6061871336344701, 0.6006329967575015, 0.5925754751648203, 0.5808950235422582, 0.5644105663702081, 0.5419267127836488, 0.5123759457604228, 0.4751183746737355, 0.43043144025293156, 0.38008020925535185, 0.32759650799188295, 0.2777575913555481, 0.2351886300617187, 0.20288571825862314, 0.1816714242970304, 0.17069750793984778, 0.16828296262253334, 0.17249611104525836, 0.18140743063906747, 0.19317928271401796, 0.20610461796094728, 0.2186044213693785, 0.22916435709398889, 0.23622278351873238, 0.23808013473390544, 0.23296469703376121, 0.2194239008812383, 0.1971133324075873, 0.1677002148212329, 0.13509396366268775, 0.10427568291398343, 0.07924563358074717, 0.06177808250062096, 0.05186721200466839, 0.04910661008352084, 0.05398370820118536, 0.06866668050121455, 0.09667948867577882, 0.14010814455355214, 0.19507285296341761, 0.2514040310494902, 0.2990186192456708, 0.3333385333302497, 0.354951345988609, 0.366768019225795, 0.3719392889242799, 0.3730100268641807, 0.37177787567756004, 0.36941386972406837, 0.36663471156418753, 0.3638523036599009, 0.3612843115758146, 0.35902970509799054, 0.35711797443189763, 0.3555401330418815]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y_PREDICATE = X.map{|e| [e]}.map {|a| a_network.get_forward_outputs(a).first}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following graph shows `X`, `Y_IDEAL`, `Y` and `Y_PREDICATED`.\n",
    "\n",
    "Looks good."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div id='vis-a097c9a8-a0b5-449b-8c2b-502dda1bd74f'></div>\n",
       "<script>\n",
       "(function(){\n",
       "    var render = function(){\n",
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       "        var id_name = '#vis-a097c9a8-a0b5-449b-8c2b-502dda1bd74f';\n",
       "        Nyaplot.core.parse(model, id_name);\n",
       "\n",
       "        require(['downloadable'], function(downloadable){\n",
       "          var svg = d3.select(id_name).select(\"svg\");\n",
       "\t  if(!svg.empty())\n",
       "\t    svg.call(downloadable().filename('fig'));\n",
       "\t});\n",
       "    };\n",
       "    if(window['Nyaplot']==undefined){\n",
       "        window.addEventListener('load_nyaplot', render, false);\n",
       "\treturn;\n",
       "    } else {\n",
       "       render();\n",
       "    }\n",
       "})();\n",
       "</script>\n"
      ],
      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot1 = Nyaplot::Plot.new\n",
    "ideal_line = plot1.add(:line, X, Y_IDEAL)\n",
    "ideal_line.title('Y_IDEAL')\n",
    "training_scatter = plot1.add(:scatter, X, Y)\n",
    "training_scatter.title('Y')\n",
    "training_scatter.color('rgb(43,140,190)')\n",
    "predicated_line = plot1.add(:line, X, Y_PREDICATE)\n",
    "predicated_line.title(\"Y_PREDICATED\")\n",
    "predicated_line.color('#f85')\n",
    "plot1.legend(true)\n",
    "plot1.show"
   ]
  }
 ],
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